As the world has become more reliant on computers and information exchange, the need for the reliable transmission of data has become increasingly important. One key element in information exchange is the accurate and efficient transmission and reception of data across noisy transmission channels.
Signal processing methods implemented in practical communications systems are usually designed under the assumption that any underlying noise and interference is Gaussian. Although this assumption finds strong theoretical justification in the central limit theorem, the noise and interference patterns commonly present in modem mobile communications systems are far from Gaussian. Noise and interference generally exhibit “impulsive” behavior. In typical mobile communication systems, noise and interference sources often include motor-vehicle ignition noise, switching noise from electromechanical equipment, thunderstorms, and heavy bursts of interference. Current signal processing systems are not designed to handle these non-Gaussian noise sources. Accordingly, these systems may perform poorly, and might even fail, in the presence of impulsive noise.
Channel noise and interference can be effectively modeled as the superposition of many small and independent effects. In practice, these effects do not always follow a Gaussian distribution. This situation appears to contradict the central limit theorem. For many years, engineers have been unable to explain this apparent contradiction. Consequently, many of the techniques developed to cope with impulsive noise were mainly ad hoc, largely based on signal clipping and filtering prior to application of a Gaussian-based technique.
Clipping the amplitude of an input signal is only effective if the amplitude of the input signal is above or below the specific threshold values. These threshold values are typically determined by the limits of the hardware used in a receiver in a communications system. Accordingly, the threshold values are often chosen to take advantage of the full dynamic range of the analog to digital (A/D) converter(s) of the receiver. However, if impulsive noise, added to the input signal, does not cause the amplitude of the signal to exceed a specific threshold, clipping will not remove the noise. Additionally, even when noise does cause the signal to exceed the threshold, the clipping solution only removes the noise to the extent that the magnitude of the signal plus the noise is above the threshold. Accordingly, noise is not actually removed, but its effects are somewhat reduced.
When individual signals within a sequence are contaminated by noise, the sequence may not be properly decoded and efficient communications may be difficult. In typical communication systems, decoding is used to identify potential communication errors. Additionally, decoding may be able to correct some, or even most, errors. Errors may be corrected by one of many error detection and correct schemes known to those skilled in the art. Typical coding and decoding schemes are able to correct errors by inserting controlled redundancy into the transmitted information stream. This is typically performed by adding additional bits or using an expanded channel signal set. These schemes allow the receiver to detect and possibly correct errors.
In its most simple form, one problem with noisy transmission environments is that, a certain percentage of the time, a transmitted ‘1’ is received as a ‘0’ or vice versa. There are many methods of encoding data that allow received errors to be detected or even corrected. These encoding and decoding schemes are typically optimized based on a set of underlying assumptions. Preferably, these assumptions are designed to match the conditions of a real-world communications environment. Generally, decoding systems are designed under the assumption that the underlying noise and interference is Gaussian. When these assumptions do not match real-world conditions, the performance of such schemes may no longer be optimal. In real-world environments, this effect is ever-present because conditions are constantly changing. Even those systems in existence that try to accommodate impulsive noise are based on average conditions. These systems fall short of optimal performance when conditions stray from the average. These problems are compounded in a mobile system because conditions change even more rapidly and more often than stationary systems.
When a receiver is able to detect, but not correct, errors in received information, the receiver may request that the transmitter resend the information. In a noisy environment, this may lead to highly inefficient communications.
There is a need in the art for systems and methods for processing signals to alleviate impulsive noise distortion.
Additionally, there is a need in the art for systems and methods for providing an adaptive decoding system for efficient communications in non-Gaussian, non-stationary environments.